Project Overview
This project is an NLP (Natural Language Processing) application that analyzes restaurant reviews and classifies them as either:
Positive (موجب) ?
Negative (سالب) ?
The main goal of the project is to help restaurant owners and customers understand public opinion by automatically evaluating written reviews.
? Project Objective
Analyze text reviews written by customers
Determine the sentiment of each review (Positive / Negative)
Apply NLP techniques to convert text into a format understandable by machine learning models
? Technologies & Concepts Used
Natural Language Processing (NLP)
Text Preprocessing
Tokenization
Stopwords Removal
Text Cleaning
Feature Extraction
Bag of Words (BoW) / TF-IDF
Machine Learning Algorithms
Logistic Regression / Naive Bayes / SVM (حسب البروجكت)
?️ Tools & Libraries
Python ?
NLTK / SpaCy
Scikit-learn
Pandas & NumPy
? Dataset
The dataset consists of restaurant reviews written in text format
Each review is labeled as:
1 → Positive Review
0 → Negative Review
⚙️ How It Works
Load and preprocess the dataset
Clean the text (remove punctuation, stopwords, etc.)
Convert text into numerical features
Train the machine learning model
Predict sentiment of new reviews
✅ Example
Input Review:
"The food was amazing and the service was excellent"
Output: ✔️ Positive Review
? Future Improvements
Add Neutral sentiment
Support Arabic reviews
Deploy the model as a web application
Improve accuracy using deep learning models
?? Author
Ahmed Elrouby NLP & Machine Learning Student
⭐ Notes
This project was developed for learning and practicing Natural Language Processing concepts and applying them to real-world data.
